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About This Role
Senior Applied AI Engineer – Data Science \& Analytics
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An active TS//SCI clearance with polygraph is required for this role
Hey!
Bitwise is a leading provider of mission\-focused intelligence solutions that advance national security for the Intelligence Community and Department of Defense. We're a small and growing company, so you can expect to hop in on the ground floor with us and be a consequential member of the team. You'll be more than a contract performer for us — you'll also be asked for ideas to improve our company and improve your career, and you'll contribute to our team culture. We value growth and community above almost all else, so we gather regularly for game nights, happy hours, tech talks, and plenty more. We think you'll like it here!
Remember, Bitwise is not a cult.
What We Look For
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Bitwise hires talented engineers who are driven by purpose and who value a culture of technical excellence, growth, and overall wellness. We deliver new and innovative intelligence solutions to our customers at the very forefront of our country's national security missions. And we do it every single day. Our work matters, and so will you.
We ask that every new hire be able to:
- Contribute meaningful thought leadership — if not right away, over time
- Interact with our customers and earn their confidence in your abilities
- Be detailed, even if that means taking a little more time to get it right
- Contribute their ideas and ideals to improve all aspects of our company
- Allow us to invest in their education so we both grow together
- Know and live our core values every single day
About this Role
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We're looking for a Senior Applied AI Engineer with a strong data science and analytics background to join a fast\-moving AI team delivering mission\-critical capabilities to analysts and operators who need to make better, faster decisions. This is not a back\-office research role — you'll work directly with mission customers, dig into real operational problems, and rapidly prototype AI solutions that create tangible impact.
You'll combine data science, machine learning, and software engineering to transform large, complex datasets into actionable mission insights. The team moves fast, values customer collaboration, and favors practical AI solutions over lengthy development cycles. If you're energized by the idea of seeing your work matter in a high\-stakes environment, you'll fit right in here.
In this role, you will:
- Design and develop AI\-enabled analytics applications using Python and modern software engineering practices
- Perform exploratory data analysis, feature engineering, statistical analysis, and machine learning on mission datasets
- Develop pattern\-of\-life, anomaly detection, clustering, and predictive analytics capabilities
- Build retrieval\-augmented generation (RAG) and LLM\-powered workflows that enhance analytical processes
- Collaborate directly with mission customers to understand their datasets and develop AI\-driven analytical solutions
- Integrate structured and unstructured data into scalable AI applications
- Evaluate emerging AI and machine learning techniques for mission applicability
- Mentor junior engineers and contribute to technical direction across the team
Requirements
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- 12 years of relevant experience, or 16 years in lieu of a B.S. in a technical discipline
- Experience developing production software using Python
- Experience with data science, machine learning, or statistical analysis
- Hands\-on experience with libraries such as Pandas, NumPy, Scikit\-learn, or similar
- Experience building AI or machine learning applications
- Experience working with SQL, Elasticsearch, or other large\-scale data platforms
- Strong analytical and problem\-solving skills
- Ability to communicate technical concepts clearly to both technical and non\-technical stakeholders
- Experience collaborating directly with customers to solve analytical problems
- Active TS//SCI clearance with polygraph
Preferred Skills
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- Experience with LLMs, prompt engineering, or retrieval\-augmented generation (RAG)
- Experience with geospatial analysis, pattern\-of\-life analytics, or time\-series analysis
- Experience with data visualization tools and dashboard development
- Experience deploying machine learning models into production environments
- Experience supporting classified mission environments
️ Benefits at Bitwise
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- Top salaries because we're top performers
- Pick your PTO — Everyone values time and money differently, so we give you the flexibility to choose between 3 and 5 weeks of PTO with a corresponding adjustment to your pay. Your choice, your balance.
- All 11 federal holidays, paid!
- Up to 2 snow days, paid!
We'll quadruple (4x!) the first 6% you contribute to your 401(k), giving you up to a 24% company match. Contributing less than 6%? Unclaimed matches come right back to you as extra income, giving you a guaranteed 24% that goes to your retirement, to your paycheck, or both. C'mon now!
- 100% employer\-paid medical, dental, vision, life, and disability insurances. That's a lot. Already covered on health insurance? No problem — we'll trade you this benefit for a boost to your salary instead.
- $5,250 annual education assistance for training, certifications, tuition, and even student loan repayments.
Spot bonuses for obtained certifications, customer recognition, and just about anything else that makes us go "Hot damn!". We hope to say that many times about you.
Salary Range
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$282,000 – $332,000
This range includes our flex income and depends upon your annual work hours.
Salary is determined on an individual basis following the interview process and considering various factors such as years of experience, skills, education, and certifications.
️ What to Expect After Applying
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You'll get an email from us right away to acknowledge your application. After that, we'll:
- Send over additional company materials for you to review
- Schedule a video call to talk about you, our company, and your place in it
- Craft a delightful offer package for you and record a video walkthrough
- Work closely with you through onboarding. We'll keep it joyful and easy.
ℹ️ Disclaimers
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Bitwise is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other legally protected status. All employment decisions are based on qualifications, merit, and business needs.
Compensation for this position, including all compensation options, will be determined on an individual basis following the interview process.
If you require accommodations at any stage of the application or interview process, please reach out to us.
Last updated: 8/10/26
Salary Context
This $282K-$332K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bitwise, LLC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($307K) sits 43% above the category median. Disclosed range: $282K to $332K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bitwise, LLC AI Hiring
Bitwise, LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Columbia, MD, US. Compensation range: $332K - $332K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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